Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 64, Issue 11, Pages 2937-2949Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2015.2444237
Keywords
Battery health prognostics; Bayesian inference; generative topographic mapping (GTM); remaining useful life (RUL) prediction; state-space model (SSM)
Funding
- National Nature Science Foundation of China [51375290, 71001060]
- Shanghai Municipal Education Commission [13YZ002]
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In this paper, a battery health prognostics system is developed based on Bayesian-inference probabilistic (BIP) indication and state-space model (SSM) that integrates logistic regression (LR) and particle filtering (PF). In this system, generative topographic mapping is constructed to model distribution of multisensor data from healthy battery under an assumption that predictable fault patterns are not available. BIP is developed as a quantification indication of battery state-of-health. BIP is capable of offering failure probability for the monitored batteries, which has intuitionist explanation related to health degradation state. SSM is used for modeling health propagation of battery on the time flow, where LR and PF are integrated to predict remaining useful life of the battery. The experimental results on a lithium-ion battery testbed illustrate the potential applications of the proposed system as an effective tool for battery health prognostics.
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